H200 vs T4

Memory capacity defines a core disparity: the H200's 141 GB HBM3e VRAM supports massive models that exceed the T4's 16 GB GDDR6 limit, enabling larger batch sizes in training and inference. Bandwidth amplifies this: 4800 GB/s on the H200 versus 320 GB/s on the T4 allows faster data movement, reducing bottlenecks in memory-intensive operations like LLM processing.

Compute metrics reveal architecture advantages. The H200's FP16 performance at 1979 TFLOPS excels in mixed-precision training, where FP16 accelerates convergence without full FP32's 67 TFLOPS overhead; the T4's balanced 8.1 TFLOPS in both suggests limitations for modern scaled workloads. FP8 at 3958 TFLOPS on the H200 further boosts inference efficiency for quantized models.

Power draw underscores trade-offs: the H200's 700W TDP suits data centers, while the T4's 70W enables dense deployments. In practice, the H200 handles enterprise-scale AI with 15 times the VRAM and bandwidth, transforming batch sizes from tens to thousands compared to T4 constraints.

The H200 stands out for large-scale AI training and inference where models demand over 16 GB VRAM. Its 141 GB capacity and 4800 GB/s bandwidth support batch sizes for LLMs exceeding 100 billion parameters, with 1979 TFLOPS FP16 enabling rapid iterations. Cloud users prioritizing throughput over cost select it across 26 offers averaging $3.62 per hour.

High-performance computing tasks benefit from NVLink and PCIe 5.0 interconnects, unavailable on the T4.

The T4 excels in cost-sensitive, low-power inference for smaller models fitting within 16 GB GDDR6. Its 70W TDP allows high-density deployments, and 8.1 TFLOPS FP16/FP32 suffices for real-time tasks like edge analytics. At an average $1.66 per hour across 6 offers, it provides value for legacy or lightweight workloads.

PCIe form factor suits standard servers without specialized cooling.